Anomaly Detection
Random Cut Forest

Random Cut Forest

RCF detects anomalous data points within a data set that diverge from otherwise well-structured or patterned data. This algorithm takes a bunch of random data points cuts them into the same number of points and creates trees. If we combine all trees creates a forest of data points to determine that if a particular data point is an anomaly or not.

Parameters

  • time_decay(Default:1/2560) → Determines how long a sample will remain before being replaced.

  • number_of_trees(Default:50) → Number of trees to use.

  • output_after(Default:64) → The number of points required by stream samplers before results are returned.

  • sample_size(Default:256) → The sample size used by stream samplers in this forest .

Example Usage

import turboml as tb
rcf_model = tb.RCF()
MStreamAMF Regressor